The interaction between the solar wind and the Magnetosphere can produce Geomagnetically Induced Currents (GIC’s) on the ground, which are capable of causing power outages and damage to crucial infrastructure.
• The ability to predict when and where these events may occur could allow us to avoid the worst of this damage.
• The use of physics-informed machine learning models can offer a computationally inexpensive method of predicting GIC
events using horizontal dB/dt as a proxy, though most models thus far have fallen short of consistently accurate predictions. dB/dt was defined as:
dB_H/dt = sqrt(dE^2/dt + dN^2/dt)
With N and E the North and East components of the magnetic field respectively.
• Here, a Long-Short Term Memory (LSTM) model was used to determine the risk of dB/dt going over thresholds of 9, 18,
42, 66, and 90 nT/min for the Ottawa (OTT) ground magnetometer station.
• Three storms were chosen for testing and removed from the training set: March 30, 2001 (~ -211nT), December 14,
2006 (~ -437nT), & August 05, 2011 (~ -126nT).
• The storms were chosen for several reasons; they represent different storm intensities, they occurred at different points
in the solar cycle, and there are minimal gaps in the data that needed to be interpolated over.
